


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						None
    __magic_name__         =						BloomTokenizerFast
    __magic_name__         =						BloomTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						False
    __magic_name__         =						'tokenizer_file'
    __magic_name__         =						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}
    def 						a_     (			self       ):
         super().setUp()
         snake_case											=							BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self     ,   **__snake_case       ):
         kwargs.update(self.special_tokens_map       )
         return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_rust_tokenizer()

         snake_case											=							['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
         snake_case											=							[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

         snake_case											=							tokenizer.batch_encode_plus(__snake_case       )['''input_ids''']
         self.assertListEqual(__snake_case     ,   __snake_case       )

         snake_case											=							tokenizer.batch_decode(__snake_case       )
         self.assertListEqual(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   __snake_case=6       ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   # tokenizer_r.pad_token = None # Hotfixing padding = None
                   # Simple input
                   snake_case											=							'''This is a simple input'''
                   snake_case											=							['''This is a simple input 1''', '''This is a simple input 2''']
                   snake_case											=							('''This is a simple input''', '''This is a pair''')
                   snake_case											=							[
                       ('''This is a simple input 1''', '''This is a simple input 2'''),
                       ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                   ]

                   # Simple input tests
                   try:
                        tokenizer_r.encode(__snake_case     ,   max_length=__snake_case       )
                        tokenizer_r.encode_plus(__snake_case     ,   max_length=__snake_case       )

                        tokenizer_r.batch_encode_plus(__snake_case     ,   max_length=__snake_case       )
                        tokenizer_r.encode(__snake_case     ,   max_length=__snake_case       )
                        tokenizer_r.batch_encode_plus(__snake_case     ,   max_length=__snake_case       )
                   except ValueError:
                        self.fail('''Bloom Tokenizer should be able to deal with padding'''       )

                   snake_case											=							None  # Hotfixing padding = None
                   self.assertRaises(__snake_case     ,   tokenizer_r.encode     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''       )

                   # Simple input
                   self.assertRaises(__snake_case     ,   tokenizer_r.encode_plus     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''       )

                   # Simple input
                   self.assertRaises(
                       __snake_case     ,   tokenizer_r.batch_encode_plus     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''     ,   )

                   # Pair input
                   self.assertRaises(__snake_case     ,   tokenizer_r.encode     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''       )

                   # Pair input
                   self.assertRaises(__snake_case     ,   tokenizer_r.encode_plus     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''       )

                   # Pair input
                   self.assertRaises(
                       __snake_case     ,   tokenizer_r.batch_encode_plus     ,   __snake_case     ,   max_length=__snake_case     ,   padding='''max_length'''     ,   )
    def 						a_     (			self       ):
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							load_dataset('''xnli'''     ,   '''all_languages'''     ,   split='''test'''     ,   streaming=__snake_case       )

         snake_case											=							next(iter(__snake_case       )       )['''premise''']  # pick up one data
         snake_case											=							list(sample_data.values()       )

         snake_case											=							list(map(tokenizer.encode     ,   __snake_case       )       )
         snake_case											=							[tokenizer.decode(__snake_case     ,   clean_up_tokenization_spaces=__snake_case       ) for x in output_tokens]
         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
         # any sequence length constraints. This test of the parent class will fail since it relies on the
         # maximum sequence length of the positoonal embeddings.
         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )     ,   1       )
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )     ,   1       )








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




